PushToProd.ai

PushToProd.ai is an intelligent GitHub App designed to streamline the code review process by automatically generating detailed, context-aware pull request (PR) summaries. By analyzing code changes, the tool provides clear and professional descriptions as soon as a PR is opened or reopened.

This automation not only saves developers time but also enhances team communication and improves the quality of code reviews by helping reviewers quickly understand the intent and impact of each change.

Whether it's a bug fix, feature addition, or refactor, PushToProd.ai ensures that every PR is well-documented, consistent, and easy to review without requiring any extra effort from the developer

Problem & Motivation

In software development, misalignment between developers and product managers remains a persistent challenge.

Product managers aim to provide clear, actionable requirements but may lack a complete understanding of the technical nuances involved in implementation.

Developers strive to write high-quality code that meets those requirements but can unintentionally diverge from the product manager’s vision.

This disconnect often leads to:

• Wasted time and resources
• Delayed product launches
• Misaligned deliverables that impact user satisfaction and business goals

A major contributor to this issue is the lack of shared understanding between development teams and stakeholders regarding core functionality, resulting in increased costs, rework, and missed market opportunities.

At PushToProd.ai, we tackle this challenge head-on by bringing clarity and alignment between product and engineering teams through intelligent automation and AI-driven insights.

The workflow for a seamless communication layer between product and engineering

Data Source & Data Science Approach

To build PushToProd, we focused on solving a clear and common problem: misalignment between engineering and product teams. Our primary data source was real-world workflows from platforms like GitHub and Jira. We simulated and annotated pairs of product requirements and code changes to train and test our models.

Our AI system is designed to analyze both code and product specs. We used a dual-input model approach that processes code snippets and Jira tickets in parallel. These are compared using semantic similarity algorithms to generate insights on alignment between intent and implementation.

The architecture includes:

• Jira Integration to pull in requirements.

• GitHub Integration to collect pull request (PR) code.

• ML Models deployed via Google Cloud to summarize and compare code vs. requirements.

• Syntropy Engine to match and evaluate PRs based on correctness, ambiguity, and alignment.

PushToProd integrates product requirements from Jira and tracks code changes and pull requests in GitHub. It intelligently summarizes the code updates, maps them back to the original Jira tickets, synchronizes information across both platforms, and issues notifications ensuring alignment across teams while minimizing the need for manual communication or meetings.

Workflow-Internal

Toy Example

We categorized our evaluation framework into five key product areas: Product Functionality, System Architecture, Code Quality, UX, and Trust & Compliance. This helped ensure our app could adapt across diverse software development environment

Architecture Overview

Evaluation

Our evaluation focused on how accurately our system could match code changes to product requirements across different difficulty levels and categories. We conducted extensive Exploratory Data Analysis (EDA) using simulated code requirement pairs judged by our AI models.

Key Findings:

• Beginner-level tasks had an 80% match rate between code and requirements.

• Advanced tasks had a slightly lower accuracy of 74%, due to increased complexity.

Performance by Category:

• High accuracy in:

  • Readability

  • Maintainability

  • Core business logic

  • Adherence to standards

• Lower accuracy in:

  • Compliance and regulatory needs

  • Error handling and user experience

  • Testing and validation criteria

These insights guided our refinement of the summarization engine and helped us understand where human input might still be needed.

Key Learnings & Impact

• Developers save time by avoiding manual summaries and updates.
• PMs get clearer insight without needing technical fluency.
• Teams stay aligned in their existing tools Jira and GitHub with no need to switch platforms.
• Our approach reduces friction, increases code quality, and improves documentation.

Acknowledgements

Special thanks to our UC Berkeley W210 instructors and project advisors. We’re especially grateful to Joyce Shen and Zona Kostic for sharing their time and expertise.